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"causal hypotheses" is a correct and usable term in written English.
It is commonly used in academic contexts to describe hypotheses that are proposed to explain the cause of something. For example: "The researcher proposed three causal hypotheses to explain the observed patterns in the data."
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This is precisely how we model competing causal hypotheses.
This is precisely how we model competing causal hypotheses. .
Testing causal hypotheses and theories requires that alternative explanations of test predictions can be ruled out.
It is committed to specific causal hypotheses in terms of abnormalities in underlying neurobiological systems.
They already take causal hypotheses as inputs and refine them in an iterative way (Grasshoff 2011).
Probability trees can encode alternative causal realizations, and in particular alternative causal hypotheses (Shafer [1996]).
Axhausen applied SEM to causal hypotheses linking car ownership, season ticket ownership, and modal usage [33].
Both the interventions and the constraints on the causal hypotheses introduce statistical asymmetries that permit the extraction of causal information.
In this paper we show that it can also be a powerful representational tool for a wide variety of causal hypotheses in such domains.
Conceptual models are used to illustrate the linkages among management (societal) actions, environmental stressors, and societal/ecological effects, and provide the basis for developing and testing causal hypotheses.
Probabilistic models of expected information gain require integrating prior knowledge about causal hypotheses with knowledge about possible actions that might generate data relevant to those hypotheses.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com